No-Code Machine Learning with Qlik AutoML

Post published:2 November, 2025
Post category:StudyBullet-22
Reading time:5 mins read

Learn Machine Learning Concepts, Build your Model & get accurate predictions without writing any Code using Qlik AutoML
⏱️ Length: 2.7 total hours
⭐ 4.06/5 rating
👥 53,818 students
🔄 March 2022 update

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Course Overview
- This comprehensive yet compact course uniquely positions business users and data analysts to leverage the power of machine learning without any coding, utilizing the intuitive Qlik AutoML platform. It’s designed for rapid transformation of raw data into predictive insights, democratizing AI for immediate business impact.
- Discover a streamlined, visual methodology for building, training, and deploying machine learning models, focusing squarely on practical problem-solving in a business context. The curriculum emphasizes efficiency and accessibility, enabling quick generation of forecasts and classifications.
- Explore an end-to-end no-code workflow from data ingestion to actionable predictions within the Qlik ecosystem. Learn to iterate on models swiftly, understanding how different data inputs and configurations influence predictive accuracy for diverse business applications.
- Ideal for professionals seeking to integrate robust, data-backed predictive capabilities into their existing business intelligence initiatives, fostering a proactive approach to strategic planning and operational optimization.
Requirements / Prerequisites
- Zero Coding Experience Required: This course is specifically engineered for non-technical individuals, making complex machine learning concepts accessible through a purely visual interface.
- Basic Data Familiarity: A foundational understanding of how business data is structured (e.g., tables, fields, records) will aid in contextualizing and preparing datasets for model training.
- Enthusiasm for Practical Application: A keen interest in applying machine learning to solve real-world business challenges and a willingness to engage with hands-on exercises are the key prerequisites.
- Web Application Comfort: General computer literacy and comfort navigating cloud-based platforms are beneficial, as Qlik AutoML is a web-based service.
Skills Covered / Tools Used
- Skills Covered:
  - Business Problem Identification: Develop the acumen to pinpoint suitable business questions that can be effectively addressed using predictive machine learning models, moving beyond simple retrospective analysis.
  - Data Preparation for AutoML: Gain proficiency in structuring, cleaning, and validating diverse datasets to ensure optimal input for automated machine learning algorithms, crucial for model reliability.
  - Model Output Interpretation: Master the skill of critically evaluating and translating complex model diagnostics and predictions into clear, actionable business insights for stakeholders.
  - Iterative Model Refinement: Learn systematic approaches to enhancing model performance and accuracy through guided adjustments and re-evaluation cycles within a no-code environment.
  - Predictive Communication: Acquire techniques for effectively presenting machine learning forecasts and classifications to business leaders, enabling informed strategic decision-making and operational adjustments.
  - Ethical ML Foundations: Understand fundamental considerations regarding potential biases in data and models, fostering responsible and fair application of AI in organizational contexts.
  - Scenario-Based Analysis: Develop expertise in conducting ‘what-if’ simulations using trained models to explore potential business outcomes under varying conditions, facilitating proactive planning.
- Tools Used:
  - Qlik AutoML: The central and exclusive platform for the entire machine learning lifecycle, encompassing data ingestion, model training, evaluation, deployment, and making predictions via its intuitive visual interface.
  - Qlik Sense (Implicitly): While AutoML is the primary tool, an understanding of its integration potential with Qlik Sense enhances the full analytical workflow, allowing for visualization and dashboarding of ML-driven insights.
Benefits / Outcomes
- Rapid Insight Generation: Significantly accelerate the process from raw data to actionable business intelligence, delivering predictive insights much faster than traditional, code-heavy ML approaches.
- Empowered Independent Analysis: Gain the capability to independently generate accurate forecasts and classifications, reducing dependency on specialized data science teams for common predictive tasks.
- Democratized AI Adoption: Become an agent for AI transformation within your organization, showcasing how advanced analytics can be accessible and beneficial across all departments.
- Enhanced Business Value Creation: Apply predictive models to optimize critical business functions, improve customer strategies, identify emerging trends, and mitigate risks, directly contributing to organizational growth.
- Future-Proofed Skillset: Equip yourself with highly relevant, in-demand skills in no-code AI and predictive analytics, boosting your professional value and career trajectory in a data-driven economy.
- Agile Solution Prototyping: Develop the agility to quickly test hypotheses and prototype new predictive solutions, enabling rapid iteration and adaptation to evolving business requirements.
PROS
- Exceptional Accessibility: Makes complex machine learning concepts and applications available to a broad audience, including business analysts and domain experts without any programming background.
- High Speed-to-Value: Facilitates incredibly fast development, training, and deployment of predictive models, leading to quicker insights and more agile business responses compared to traditional ML.
- Direct Business Relevance: The course is meticulously designed around real-world business problems, ensuring all acquired skills are immediately applicable and impactful in professional roles.
- Intuitive User Interface: Leverages Qlik AutoML’s visual, guided platform, significantly lowering the learning curve and making sophisticated model building straightforward and user-friendly.
- Cost-Efficiency: Empowers existing personnel to perform advanced analytics, potentially reducing the need for expensive, dedicated data science resources for many common predictive tasks.
- Solid Foundational Understanding: Provides a robust conceptual grasp of ML principles, model evaluation, and interpretation, even without coding, fostering a deeper understanding of AI mechanics.
CONS
- Limited Customization for Advanced Research: While powerful for business applications, a no-code platform may offer less granular control over algorithms or bespoke model architectures required for highly specialized academic research or cutting-edge ML development.